Responsibilities

Embedded AI Deployment 

• Assist senior engineers in integrating and deploying trained AI/ML models onto embedded/edge platforms.

• Develop and modify software components using C/C++ and Python.

• AI model conversion, operator compatibility checks, custom operator development and integration with appropriate inference runtimes and hardware platforms.

• Build, run and test software on development boards or target hardware.

• Investigate software/model integration issues with guidance from experienced engineers.

• Develop integration tests and simple automation scripts.

Competencies:

• Discipline Design Process Application: Demonstrate proficiency in applying the established Discipline design processes and methodologies.

• Technical Learning: Actively develop knowledge in embedded systems, deep learning inference, AI deployment frameworks, target hardware architectures and software optimisation techniques through project work, mentoring and self-directed learning.

Development Automation and Efficiency

• Use state of art development tools and develop automated processes for software builds, testing, model conversion, deployment and benchmarking. 

Innovation:

• Contribute ideas, experiments and prototypes to investigate technical problems & potential improvements. Innovation is expected, with technical direction and review provided by experienced engineers.

Collaboration and Influence:

• Effective Communication: Communicate effectively with team members and stakeholders, ensuring clear and concise information exchange.

Profile

Education

Honours Bachelor's degree (NFQ Level 8 or equivalent) in Computer Engineering, Electronic/Electrical Engineering, Computer Science, Software Engineering, Artificial Intelligence, Robotics or a related technical discipline.
A Master's degree is beneficial but not required.
 

Experience

Applications from recent graduates with no commercial engineering experience are welcome. Relevant experience may include internships, university projects, final-year projects, research projects, hackathons, substantial personal/open-source projects, and maximum one year in a company after graduation.

Skills

• Basic understanding of programming and software engineering principles.

• Working knowledge of C or C++ through academic, internship or personal-project experience.

• Working knowledge of Python.

• Familiarity with a Linux development environment.

• Familiarity with Git/version control.

• Basic understanding of machine-learning concepts, including training versus inference, datasets and model evaluation.

• Exposure to at least one ML framework such as PyTorch or TensorFlow.

• Basic understanding of System on Chip (SOC) architecture, embedded systems or constrained computing.

• Good analytical and problem-solving skills.

• Ability to communicate technical information clearly.

• Strong willingness to learn unfamiliar technologies.

Desirable / nice to have skills (not mandatory)

• ONNX / ONNX Runtime

• TensorFlow Lite

• PyTorch / ExecuTorch

• Model quantisation

• Pruning or model compression

• ARM-based processors

• GPU / NPU / DSP accelerators

• Embedded Linux

• RTOS

• Cross-compilation

• CMake

• Docker

• CI/CD

• Profiling/debugging tools

• SIMD/vectorisation

• Hardware/software interfacing